Abstract
The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.
Highlights
The East Coast Economic Region (ECER) of Malaysia is a unique mix industrial region, which plentiful endowment of nature and agricultural resources
Kuantan River Basin located at the ECER is one of the vital tributaries, which irrigates the majority of the rural, urban agriculture and industrial areas of Kuantan District
The daily precipitation time series of monitoring stations located in the Kuantan River Basin are selected due to the quality of time series for this district is frequently degraded by the missing data
Summary
The East Coast Economic Region (ECER) of Malaysia is a unique mix industrial region, which plentiful endowment of nature and agricultural resources. Burhanuddin et al [3] proposed a multiple imputation algorithm, which associate the conventional normal ratio algorithm and moving block bootstrapping. The analysis results rendered their proposed algorithm yielded a more reliable results compared to the single imputation algorithm in missing daily precipitation data treatment. The imputation algorithms proposed by Burhanuddin et al [3, 4] are highly dependent on the homogeneous precipitation time series of neighbouring monitoring stations. Saeed et al [9] proposed median algorithm, which the proposed single imputation algorithm is without depending on the homogeneous precipitation time series of neighbouring monitoring stations. This study intended to develop another multiple imputation algorithm without depending on the homogeneous precipitation time series of neighbouring monitoring stations.
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